Datamining for marketing: a real simple explanation of LIFT

In using dataminig models for marketing campaigns, a definition of lift could be:

“lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.” (wikipedia)

Yeah, wright!

Now in plain language for a normal human being:

“lift” means: “how much times is my model better than a random selection”.

Example:

Divide you customer database at random in 3 equal parts: part I , part II , and part III

– when you use no model at all: take a random selection A of 10,000 persons out of part I. Send them an email in an effort to sell something. Let’s assume that 2% of them bought you product. Result (no model)=2%

– use your first datamining model (model M1) to select 10,000 persons out of part II. Send them the same email. Let’s assume that 6% of them bought your product. Result (M1)=6%

– use another datamining model (model M2) to select 10,000 persons out of part III. Send them the same email. Let’s assume that 10% of them bought your product. Result (M2)=10%

It is straightforward to see that model M1 does 3 times better than without a model and model M2 does 5 times better.

Formula: lift= ( result of Model X) divided by (result of no model at all)